The advertisement landscape is experiencing major changes. Whereas today's advertisement can be considered as an area wide broadcasting (e.g. nation-wide newspaper ads or TV commercials), the target for advertisers is to understand both, to whom advertisements are targeted and in which context. The former one, on the one hand, requires understanding consumers and their behavior at ever more granular levels. The latter one, on the other hand, requires understanding where the consumer is, what the consumer is doing and where the consumer is going. Location technologies play a key role in each of these.
While novel positioning technologies are already available, as will be described in the following, they have mainly been developed for navigation purposes. This can render the respective technology only partly useful for other scenarios or use cases, such as for the advertising use case, described above.
Such novel positioning systems and solutions are specifically developed (and if necessary also deployed) mainly for the purpose of navigation. The traditional positioning technologies, which are mainly used outdoors, i.e. satellite and cellular positioning technologies, cannot always deliver the desired performance that would enable seamless and equal positioning experience at all time, particularly indoors or in vehicles. As examples, required positioning accuracy (2-3 m), coverage (˜100%) and height detection are challenging to achieve with satisfactory performance levels with the systems and signals that were not designed and specified for every use case in the first place. For instance, in case of indoor or underground situations, satellite-based radio navigation signals simply do not penetrate through the walls and roofs for the adequate signal reception and the cellular signals often have a too narrow bandwidth for accurate ranging by default. But also in certain outdoor scenarios, there may be the case of insufficient coverage of e.g. satellite-based radio navigation, for instance in case of bad weather, in urban street canyons or in tunnels.
Several dedicated solutions have already been developed and commercially deployed during the past years e.g. solutions based on technologies like pseudolites (GPS-like short-range beacons), ultra-sound positioning, Bluetooth or Bluetooth LE signals and WLAN fingerprinting. What is typical to these solutions is that they require either deployment of totally new infrastructure (such as beacons or tags) or manual exhaustive radio-surveying of the streets and buildings including all the floors, spaces and rooms. This is rather expensive and will take a considerable amount of time to build the coverage to the commercially expected level, which can in some cases narrow the potential market segment to only a very thin customer base e.g. for health care or dedicated enterprise solutions. Also, the diversity of these technologies makes it difficult to build a globally scalable indoor positioning solution, and the integration and testing will become complex if a large number of technologies needs to be supported in the consumer devices, such as smartphones.
For a positioning solution to be commercially successful in various use cases and situations it needs to be globally scalable, have low maintenance and deployment costs, and offer acceptable end-user experience. This can best be achieved, if the solution is based on an existing infrastructure in the buildings and on existing capabilities in the consumer devices. Accordingly, such a positioning is preferably based on technologies like Wi-Fi- and/or Bluetooth (BT)-technologies that are already supported in almost every smartphone, tablet, laptop and even in the majority of the feature phones. It is, thus, required to find a solution that uses such cellular or non-cellular radio signals in such a way that makes it possible to achieve 2-3 m horizontal and vertical positioning accuracy with the ability to quickly build the global coverage for this approach.
Modern global cellular (GSM, WCDMA, TD-SCDMA, LTE, LTE-A, CDMA) and non-cellular (primarily WiFi, but also BT, BTLE, Zigbee, etc.) positioning technologies are based on collecting large global databases containing information on the cellular and non-cellular signals emitted from respective radio nodes, the “collecting phase” or “training phase”. A large portion of this data typically originates from the users of these positioning technologies, naturally with the end-user consent. It could also be possible to use volunteers to survey the sites in exchange of reward or recognition and get the coverage climbing up globally in the places and venues important for the key customers. While automated crowd-sourcing can enable indoor localization in large amount of buildings, manual data collection using special tools may be the best option, when the highest accuracy is desired.
In any case, the collected data is typically in the form of so called (radio) fingerprints, which contain a location estimate (e.g. GNSS-based or WiFi-based) and the measurement(s) taken from the radio interface(s) (cellular or non-cellular). In case of a cellular positioning technology, such measurements may contain global and/or local identifiers of the cellular network cells observed, signal strength estimates, pathloss estimates and/or timing measurements (such as Timing Advance or Round-Trip Time). In case of a non-cellular positioning technology, such measurements may contain an identifier of the non-cellular radio node (e.g. the BSSIDs, typically the MAC address of the air interface of a respective WiFi access points observed and/or the SSIDs), signal strengths estimates (e.g. received signal strength index, physical Rx level in dBm ref 1 mW, etc.), pathloss estimates and/or timing measurements (e.g. Round-Trip Time).
This data gets uploaded to the server or cloud server, where algorithms are run to generate models of wireless communication nodes for positioning purposes based on the collected fingerprints received from the multitude of the users. Such models may be coverage areas, node positions, radio propagation models, Rx fields, etc. In the end, these models are transferred back to the user terminals for use in position determination, the “positioning phase”.
Note that although the end user terminal had GNSS-capability, the end user can still benefit from using cellular/non-cellular positioning technologies in terms of time-to-first-fix and power consumption. Also, not all applications require highly accurate GNSS-based position (e.g. for local weather application it suffices to use cell-based location estimate). Also, cellular/non-cellular positioning technologies work indoors, which is generally a challenging environment for GNSS-based technologies.
Due to the increasing coverage with communication networks (e.g. using cellular telecommunication networks or WiFi networks) such radio nodes or often also deployed in mobile scenarios (e.g. being installed on buses and trains) leading to mobile radio nodes, e.g. moving WiFi access points. Additionally, such moving WiFi access points may also be (temporarily) established by mobile devices, such as mobile phones of end users (so called “personal hot spots”), for sharing an internet connection with other people or devices.
In the training or collecting phase, such moving radio nodes (e.g. WiFi access points) are considered highly poisonous to the positioning service because due to their constant movement no location information can be inferred from them. Collecting radio fingerprints with measurement information on signals from such moving radio nodes can cause significant damage to the position service, so that they are typically blacklisted from the positioning database.
However, such blacklisting also leads to a loss of potentially valuable information, in particular in view of the aims of the advertising industry and technology, which require an understanding where the consumer is, what the consumer is doing and where the consumer is going, as set out above. As explained, when considering the above moving radio nodes, there remains the problem that they would impair the quality of the positioning database when they are added to the positioning database and, moreover, that it is still difficult to differentiate between different kinds of moving radio nodes, i.e. for instance the radio node installed on a bus and e.g. the temporary personal hot spot of a user accidentally passing by.